Text-to-Image Generation
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- 평가표
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- 보존된 평가 기록
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The development of the brain's blood supply in an embryo involves a complex process with several stages. Initially, there is a network of connections between the carotid and basilar artery systems that usually disappear during development. However, in some cases, these connections remain, leading to variations in the adult cerebral circulation. Around the 24th day of embryonic life (at the 3 mm embryonic stage), the internal carotid arteries (ICA) emerge. They are formed from a combination of the 3rd branchial arch arteries and the distal segments of the paired dorsal aortae. The ICA initially provides all the blood needed by the developing brain. As the brain grows, particularly the occipital region, brainstem, and cerebellum, the blood supply from the ICA becomes insufficient, prompting the development of the posterior circulation. At this stage (4-5 mm embryonic stage), the posterior circulation, specifically the hindbrain, is supplied by two longitudinal neural arteries. These arteries receive blood from several temporary connections (anastomoses) with the ICA, including: Trigeminal artery (TA), Otic artery (OA), Hypoglossal artery (HA), Proatlantal artery (ProA). These carotid-vertebrobasilar anastomoses act as temporary bridges, providing blood flow to the developing posterior circulation until the vertebral arteries (VA) and the basilar artery (BA) are fully formed.6
The BA forms between the 5-8 mm stage by the fusion of the longitudinal neural arteries. As the posterior communicating artery develops and connects with the distal BA, the TA, OA, and HA typically regress. Unlike the TA, OA, and HA, the ProA persists until the VA are fully developed. A segment of the ProA becomes incorporated into the V3 segment of the VA and the distal portions of the occipital artery. The VA develop between the 7-12 mm stage from transverse connections between cervical intersegmental arteries, starting with the ProA and progressing downwards to the 6th intersegmental artery. This 6th intersegmental artery eventually forms the origin of the adult VA from the subclavian artery. 7-8
Persistence of the HA into adulthood, the focus of this discussion, is a rare occurrence. It is the second most common persistent carotid-vertebrobasilar anastomosis, following the persistent trigeminal artery. The failure of the HA to regress during embryological development leads to PPHA. This condition can impact the normal blood flow dynamics in the brain and, in certain cases, can be associated with cerebrovascular issues like aneurysms or ischemic events.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nithishkaviyan/Show-and-Tell-Neural-Network-Image-Caption-Generator-
https://github.com/oarriaga/neural_image_captioning
https://github.com/olaviinha/NeuralImageSuperResolution
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/omer11a/bounded-attention
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation
https://github.com/omerbt/tokenflow
MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
https://github.com/openbmb/minicpm-v
Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages
https://github.com/orpatashnik/local-prompt-mixing
Localizing Object-level Shape Variations with Text-to-Image Diffusion Models
https://github.com/oxygenlu/ratlip
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/phymhan/prompt-to-prompt
https://github.com/prakashpandey9/Text2Image-PyTorch
https://github.com/priscillalui/StackGAN-Stories
https://github.com/pskrunner14/descriptor
https://github.com/pwc-1/Paper-8/tree/main/altclip
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
https://github.com/quereste/implicit-deepfake
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/qwopqwop200/semantic-image-editing-with-null-inv
Null-text Inversion for Editing Real Images using Guided Diffusion Models
https://github.com/r-khanna/stackGAN-text-to-image-synthesis-
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/rafaelortegar/Deep-Learning-Final-Project
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/rafiahmed40/stack-adverserial-network
https://github.com/rebnej/lick-caption-bias
https://github.com/rightlit/StackGAN-v2-rev
https://github.com/rightlit/cycle-image-gan-rev
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/rishavbb/Image_Captioning
https://github.com/rishidarkdevil/daam-i2i
What the DAAM: Interpreting Stable Diffusion Using Cross Attention
https://github.com/roxanasoto/AttGanESRGAN
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/royira/linguistic-binding-in-diffusion-models
https://github.com/ruoyufeng/ccedit
CCEdit: Creative and Controllable Video Editing via Diffusion Models
https://github.com/ryudrigo/my-gen-clip
CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP
https://github.com/saiyan-world/goku
https://github.com/salesforce/doodl
EDICT: Exact Diffusion Inversion via Coupled Transformations
https://github.com/samb-t/unleashing-transformers
https://github.com/samim23/NeuralTalkAnimator
https://github.com/sanchit97/secl
A Self-explaining Neural Architecture for Generalizable Concept Learning
https://github.com/saubhik/chainer-image-captioning
https://github.com/savya08/Text-to-Image
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/scrambleegg7/Text-to-Image-Synthesis2
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.